# -*- coding: utf-8 -*-
from datetime import timedelta
from jms.ods.mysql.arbitration import jms_ods__arbitration
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
# 仲裁工单清洗表
# 调整原有清洗粒度 waybill_no --> id 即工单号，
# 一个waybill_no 含有对应多个id 的情况，使用waybill_no 清洗会干掉一部分那数据，导致工单层面的缺失

jms_dwd__dwd_arbitration_base_dt = SparkSqlOperator(
    task_id='jms_dwd__dwd_arbitration_base_dt',
    task_concurrency=1,
    pool_slots=2,
    execution_timeout = timedelta(minutes=30),
    email=['lukunming@jtexpress.com','yl_bigdata@yl-scm.com'],
    master='yarn',
    name='jms_dwd__dwd_arbitration_base_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dwd/sqs/dwd_arbitration_base_dt/execute.hql',
    driver_memory='6G',
    driver_cores=2,
    executor_cores=4,
    executor_memory='12G',
    num_executors=40,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 60,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 100,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead':              '4G',
          'spark.hadoop.hive.exec.dynamic.partition.mode': 'true',
          'spark.sql.shuffle.partitions'                     : 2000,
          'spark.executor.extraJavaOptions': '-XX:+UseG1GC -XX:ParallelGCThreads=4',
          },
    hiveconf={'hive.exec.dynamic.partition'             : 'true',  # 动态分区
              'hive.exec.dynamic.partition.mode'        : 'nonstrict',
              'hive.exec.max.dynamic.partitions'        : 300,  # 最大分区
              'hive.exec.max.dynamic.partitions.pernode': 300,  # 最大分区
              },
)

jms_dwd__dwd_arbitration_base_dt << [
    jms_ods__arbitration,
]
